This study evaluates the potential of wavelet-based texture modeling for the classification of stand age in a managed maritime pine forest using very high resolution satellite data. A cross-validation approach based on stand age reference data shows that multivariate modeling of the spatial dependence of wavelet coefficients outperforms the use of features derived from co-occurrence matrices. Simultaneously adding features representing the color dependence and leveling the dominant orientation in anisotropic forest stands enhances the classification performances. These results obtained from panchromatic and multispectral PLEIADES data confirm the ability of such wavelet-based multivariate models to efficiently capture the textural properties of very high resolution forest data and opens up perspectives for their use in the mapping of mono-specific forest structure variables.